{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     crim    zn  indus  chas    nox     rm   age     dis  rad  \\\n",
       "0           1  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1   \n",
       "1           2  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2   \n",
       "2           3  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2   \n",
       "3           4  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3   \n",
       "4           5  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3   \n",
       "\n",
       "   tax  ptratio   black  lstat  medv  \n",
       "0  296     15.3  396.90   4.98  24.0  \n",
       "1  242     17.8  396.90   9.14  21.6  \n",
       "2  242     17.8  392.83   4.03  34.7  \n",
       "3  222     18.7  394.63   2.94  33.4  \n",
       "4  222     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"dataset/boston.csv\", header=0)\n",
    "# data.drop([\"class\"], axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegression:\n",
    "    '''使用梯度下降回归算法'''\n",
    "    def __init__(self, learning_rate, times):\n",
    "        '''初始化\n",
    "        parameters\n",
    "        -----\n",
    "        learn_rate: 学习率。梯度下降步长 \n",
    "        times: 迭代次数\n",
    "        '''\n",
    "        self.learning_rate = learning_rate\n",
    "        self.times = times\n",
    "    def fit(self, X, y):\n",
    "        '''对样本进行预测\n",
    "        Parameters:\n",
    "        X: 特征矩阵，可以是List也可以是Ndarray，形状为： [样本数量,特征数量]\n",
    "        y: 标签数组\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        y = np.asarray(y)\n",
    "        # 创建权重，初始值为0(也可以试试其他值)，* 长度比X特征数多1，多出的就是w0\n",
    "        self.w_ = np.zeros(1 + X.shape[1]) # X.shape[1] 为特征数\n",
    "        # 创建损失值列表，用来每次迭代的损失值。\n",
    "        # 损失值计算公式：(1/2 )*( 预测值 - 真实值)^2\n",
    "        self.loss_ = []\n",
    "        # 开始迭代\n",
    "        for x in range(self.times):\n",
    "            # 为什么使用 self.w_[1:] ？ 因为X特征个数比w少1个，所以这里w0先去掉，点乘后再加上\n",
    "            # 矩阵可以使用*运算，但是这里是ndarray数组只能用点乘，1表示x0\n",
    "            y_hat = np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "            # 计算误差\n",
    "            error = y - y_hat\n",
    "            # 将损失值加入损失列表\n",
    "            self.loss_.append(np.sum(error**2)/2)\n",
    "            # 根据差距调整权重w_\n",
    "            # 调整为 w(j)=w(j)+learn_rate*sum((y - y_hat)*x(j))\n",
    "            # 更新w0，x看成1直接更新\n",
    "            self.w_[0] += self.learning_rate * np.sum(error * 1)\n",
    "            # ****************************************************\n",
    "            # ************  重点：这里一定要搞明白 ***************\n",
    "            # ****************************************************\n",
    "            # 使用x1到xn的某一列向量(即某一特征)的转置来更新w，也就是将w向x进拟合\n",
    "            self.w_[1:] += self.learning_rate * np.dot(X.T, error)\n",
    "    def predict(self, X):\n",
    "        '''根据参数进行预测\n",
    "        Paramaters\n",
    "        -----\n",
    "        X： [样本数量, 特征数量]\n",
    "        -----\n",
    "        Returns: 预测结果\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        result = np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StandardScaler:\n",
    "    '''对数据集进行标准化处理'''\n",
    "    def fit(self, X):\n",
    "        '''根据传递的样本，计算每个特征列的均值和标准差\n",
    "        Parameters\n",
    "        -----\n",
    "        X: 训练数据。用来计算均值和标准差\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        self.std_ = np.std(X, axis=0) # 按列计算标准差\n",
    "        self.mean_ = np.mean(X, axis=0) # 按列计算#值\n",
    "    def transform(self, X):\n",
    "        '''对给定数据进行标准化处理\n",
    "        * 将X每一列都变成标准正太分布的形式，即满足均值为0，标准差为1\n",
    "        * 标准化也叫标准差标准化，经过处理的数据符合标准正态分布。\n",
    "        \n",
    "        Parameters\n",
    "        -----\n",
    "        X: 类数组类型。待转换的数据\n",
    "        '''\n",
    "        return (X - self.mean_) / self.std_\n",
    "    def fit_transform(self, X):\n",
    "        ''' fit后transform\n",
    "        '''\n",
    "        self.fit(X)\n",
    "        return self.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1804176210461773e+210"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([-2.68833075e+099, -7.57608494e+101, -1.29174503e+100,\n",
       "       -2.67102000e+100, -3.24034515e+100, -1.81386180e+098,\n",
       "       -1.52555272e+099, -1.67597187e+100, -1.90402088e+101,\n",
       "       -9.69839266e+099, -3.06079801e+100, -1.19382162e+102,\n",
       "       -5.02206239e+100, -9.48116678e+101, -3.57226707e+100])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[116831.44,\n",
       " 2083552221174028.0,\n",
       " 4.883479637283036e+25,\n",
       " 1.14536821770387e+36,\n",
       " 2.686348040018552e+46,\n",
       " 6.300564127408661e+56,\n",
       " 1.477735116047291e+67,\n",
       " 3.465881830645266e+77,\n",
       " 8.128883677155966e+87,\n",
       " 1.9065494170189406e+98,\n",
       " 4.4716234404365476e+108,\n",
       " 1.0487751334726054e+119,\n",
       " 2.4597985390359684e+129,\n",
       " 5.769214638613026e+139,\n",
       " 1.353112339006075e+150,\n",
       " 3.1735914100271017e+160,\n",
       " 7.443345350908508e+170,\n",
       " 1.7457631703262697e+181,\n",
       " 4.09451517185836e+191,\n",
       " 9.603281119422997e+201]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LinearRegression(learning_rate=0.001, times=20)\n",
    "t = data.sample(len(data), random_state=0)\n",
    "train_X = t.iloc[:400, :-1]\n",
    "train_y = t.iloc[:400, -1]\n",
    "test_X = t.iloc[400:, :-1]\n",
    "test_y = t.iloc[400:, -1]\n",
    "lr.fit(train_X, train_y)\n",
    "result = lr.predict(test_X)\n",
    "# display(result)\n",
    "display(np.mean((result - test_y)**2))\n",
    "display(lr.w_)\n",
    "display(lr.loss_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14911890500740144"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 1.47715173e-16, -2.67726470e-03, -7.77999440e-02,  3.22218582e-02,\n",
       "       -4.15123849e-02,  7.21847299e-02, -1.22496354e-01,  3.18411097e-01,\n",
       "       -8.44203177e-03, -2.09306424e-01,  1.02744566e-01, -5.29297011e-02,\n",
       "       -1.81988334e-01,  9.71339528e-02, -3.93923586e-01])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[200.0,\n",
       " 109.56340745551384,\n",
       " 89.24537638439179,\n",
       " 79.69665810311717,\n",
       " 74.15994999919499,\n",
       " 70.76479029645887,\n",
       " 68.5986097420355,\n",
       " 67.15477786507567,\n",
       " 66.1444617646852,\n",
       " 65.4007703140392,\n",
       " 64.82595473196506,\n",
       " 64.36185638542904,\n",
       " 63.973228857262946,\n",
       " 63.638247945534275,\n",
       " 63.343061508525125,\n",
       " 63.07862649070146,\n",
       " 62.83885168788381,\n",
       " 62.619493019046615,\n",
       " 62.41748706751545,\n",
       " 62.23054285212329]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为了避免每个特征数量级的不同对梯度下降造成的影响。我们对每个特征进行标准化\n",
    "lr = LinearRegression(learning_rate=0.0005, times=20)\n",
    "t = data.sample(len(data), random_state=0)\n",
    "train_X = t.iloc[:400, :-1]\n",
    "train_y = t.iloc[:400, -1]\n",
    "test_X = t.iloc[400:, :-1]\n",
    "test_y = t.iloc[400:, -1]\n",
    "\n",
    "# 标准化\n",
    "ss = StandardScaler()\n",
    "train_X = ss.fit_transform(train_X)\n",
    "test_X = ss.fit_transform(test_X)\n",
    "ss2 = StandardScaler()\n",
    "train_y = ss2.fit_transform(train_y)\n",
    "test_y = ss2.fit_transform(test_y)\n",
    "\n",
    "# 训练 预测\n",
    "lr.fit(train_X, train_y)\n",
    "result = lr.predict(test_X)\n",
    "display(np.mean((result - test_y)**2))\n",
    "display(lr.w_)\n",
    "display(lr.loss_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rcParams[\"font.family\"] = \"SimHei\"\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(result, \"ro-\", label=\"预测值\")\n",
    "plt.plot(test_y.values, \"go-\", label=\"预测值\") # pandas读取时serise类型，我们需要转为ndarray\n",
    "plt.title(\"线性回归预测-梯度下降\")\n",
    "plt.xlabel(\"测试机样本序号\")\n",
    "plt.ylabel(\"预测房价\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ad09de4d88>]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制累计误差值\n",
    "plt.plot(range(1,lr.times+1),lr.loss_, \"o-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.002805351917207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 因为放假更新涉及多个维度，不方便可视化\n",
    "# 为了可视化，我们只选择其中一个维度（RM），并画出直线，进行拟合\n",
    "lr = LinearRegression(learning_rate=0.0005, times=20)\n",
    "t = data.sample(len(data), random_state=0)\n",
    "train_X = t.iloc[:400, 6:7]\n",
    "train_y = t.iloc[:400, -1]\n",
    "test_X = t.iloc[400:, 5:6]\n",
    "test_y = t.iloc[400:, -1]\n",
    "# 标准化\n",
    "ss = StandardScaler()\n",
    "train_X = ss.fit_transform(train_X)\n",
    "test_X = ss.fit_transform(test_X)\n",
    "ss2 = StandardScaler()\n",
    "train_y = ss2.fit_transform(train_y)\n",
    "test_y = ss2.fit_transform(test_y)\n",
    "lr.fit(train_X, train_y)\n",
    "result = lr.predict(test_X)\n",
    "# display(result)\n",
    "display(np.mean((result - test_y)**2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.05755421e-16,  6.54993957e-01])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ad09a17248>]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示rm对对于价格的影响\n",
    "plt.scatter(train_X[\"rm\"], train_y)\n",
    "# 展示权重\n",
    "display(lr.w_)\n",
    "# 构建方程 y = w0 + x*w1 = -3.05755421e-16 + x *  6.54993957e-01\n",
    "x = np.arange(-5,5,0.1)\n",
    "y = -3.05755421e-16 + x *  6.54993957e-01\n",
    "plt.plot(x, y, \"g\") # 绿色直线显示我们的拟合直线\n",
    "# *********  x.reshape(-1,1) 把一维转为二位 ****************\n",
    "plt.plot(x, lr.predict(x.reshape(-1,1)), 'r')\n",
    "\n",
    "# 可以看到我们预测的和你和的完全重合了"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
